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WO2004055636A2 - Appareil et procede pour identifier des marqueurs biologiques au moyen d'un modele informatise - Google Patents

Appareil et procede pour identifier des marqueurs biologiques au moyen d'un modele informatise Download PDF

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Publication number
WO2004055636A2
WO2004055636A2 PCT/US2003/039522 US0339522W WO2004055636A2 WO 2004055636 A2 WO2004055636 A2 WO 2004055636A2 US 0339522 W US0339522 W US 0339522W WO 2004055636 A2 WO2004055636 A2 WO 2004055636A2
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Prior art keywords
virtual
measurement
therapy
results
configurations
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WO2004055636A3 (fr
Inventor
Thomas S. Paterson
Christina Maria Friedrich
Leif Gustaf Wennerberg
Seth Gary Michelson
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Entelos Inc
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Entelos Inc
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Priority to AU2003297911A priority Critical patent/AU2003297911A1/en
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Publication of WO2004055636A3 publication Critical patent/WO2004055636A3/fr
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/41—Detecting, measuring or recording for evaluating the immune or lymphatic systems
    • A61B5/411—Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2500/00—Screening for compounds of potential therapeutic value

Definitions

  • the present invention relates generally to computer models. More particularly, the present invention relates to identifying biomarkers using a computer model.
  • Biomarkers of therapies and of normal or disease conditions can be used for numerous applications in the life sciences field.
  • a biomarker of a therapy typically refers to a biological attribute that can be associated with a particular effect of the therapy.
  • a biomarker of a therapy can refer to a biological attribute that can be evaluated to infer or predict a particular effect of the therapy.
  • Biomarkers can be predictive of different effects of a therapy. For instance, biomarkers can be predictive of effectiveness, biological activity, safety, or side effects of a therapy.
  • Identification of biomarkers can play a key role in developing, testing, and implementing therapies to treat various diseases.
  • a biomarker of a therapy can be evaluated for a human patient to predict the degree of effectiveness of the therapy for the human patient prior to a clinical trial.
  • Such a biomarker can be used to select a group of human patients for the clinical trial, such that the clinical trial can target human patients that are likely to respond well to the therapy.
  • Another biomarker of the therapy can be evaluated for a human patient during the course of the clinical trial to predict a surrogate end-point or outcome of the therapy for the human patient.
  • Such a biomarker can be used to evaluate effectiveness of the therapy during the course of the clinical trial to determine, for example, whether to abort or alter the clinical trial.
  • biomarker of the therapy can be evaluated for a human patient during the course of the clinical trial to assess biological activity of the therapy for the human patient.
  • identification of biomarkers sometimes occurred during or after conclusion of a clinical trial based on statistical analysis of results of the clinical trial.
  • identification of biomarkers generally could not function to guide design of the clinical trial itself.
  • appropriate measurements of the biomarkers may not be made during the course of the clinical trial, and potentially useful information regarding a therapy may not be obtained.
  • the present invention relates to a computer- executable software code.
  • the computer-executable software code includes code to define a set of configurations associated with a computer model of a biological system. Each configuration of the set of configurations is associated with a different representation of the biological system.
  • the computer-executable software code also includes code to apply a virtual measurement to the set of configurations to produce a result of the virtual measurement for each configuration of the set of configurations and code to apply a virtual therapy to the set of configurations to produce a result of the virtual therapy for each configuration of the set of configurations.
  • the virtual measurement is associated with a measurement for the biological system absent a therapy, and the virtual therapy is associated with the therapy.
  • the computer-executable software code further includes code to identify correlation between the results of the virtual measurement for the set of configurations and the results of the virtual therapy for the set of configurations.
  • FIG. 1 illustrates a system block diagram of a computer that can be operated in accordance with various embodiments of the invention.
  • FIG. 2 illustrates a flow chart for a process to identify one or more biomarkers of a therapy using a computer model, according to an embodiment of the invention.
  • FIG. 3 illustrates the architecture of a computer model that can be used to identify biomarkers in accordance with an embodiment of the invention.
  • FIG. 4 illustrates an example of a user-interface screen indicating a virtual patient that can be defined to represent a human patient.
  • FIG. 5 illustrates an example of a user-interface screen indicating another virtual patient that can be defined to represent a different human patient.
  • FIG. 6 illustrates an example of a user-interface screen indicating various stimulus-response tests that can be defined.
  • FIG. 7 illustrates an example of a user-interface screen indicating how a stimulus-response test can be defined.
  • FIG. 8 illustrates an example of a user-interface screen indicating various virtual measurements that can be defined.
  • FIG. 9 illustrates an example of a user-interface screen indicating plots of Forced Expiratory Volume in 1 second (FEN1) curves with respect to time.
  • FIG. 10 illustrates an example of a user-interface screen indicating how a virtual therapy can be defined.
  • FIG. 11 illustrates an example of a user-interface screen indicating various virtual measurements that can be defined to evaluate the behavior of a configuration of a computer model absent a virtual therapy and based on the virtual therapy.
  • FIG. 12 illustrates another example of a user-interface screen indicating various virtual measurements that can be defined to evaluate the behavior of a configuration of a computer model absent a virtual therapy and based on the virtual therapy.
  • FIG. 13 illustrates a flow chart to identify one or more biomarkers using a virtual therapy, according to an embodiment of the invention.
  • FIG.14 illustrates an example of a user-interface screen that indicates results of virtual measurements for various virtual patients.
  • FIG. 15 illustrates an example of a graph that plots results of a first virtual measurement for multiple virtual patients with respect to results of a second virtual measurement for the multiple virtual patients.
  • FIG. 1 illustrates a system block diagram of a computer 100 that can be operated in accordance with various embodiments of the invention.
  • the computer 100 includes a processor 102, a main memory 103, and a static memory 104, which are coupled by bus 106.
  • the computer 100 can also include a video display unit 108 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT) display) on which a user-interface can be displayed.
  • a video display unit 108 e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT) display
  • LCD liquid crystal display
  • CRT cathode ray tube
  • the computer 100 can further include an alpha-numeric input device 110 (e.g., a keyboard), a cursor control device 112 (e.g., a mouse), a disk drive unit 114, a signal generation device 116 (e.g., a speaker), and a network interface device 118.
  • the disk drive unit 114 includes a computer-readable medium 115 storing software code 120 that implement processing according to some embodiments of the invention.
  • the software code 120 can also reside within the main memory 103, the processor 102, or both. For certain applications, the software code 120 can be transmitted or received via the network interface device 118.
  • FIG. 2 illustrates a flow chart for a process to identify one or more biomarkers of a therapy using a computer model, according to an embodiment of the invention.
  • the computer model can represent any of a variety of systems that may be of interest to a user.
  • the computer model will represent a system that is based on a real-world system.
  • the computer model can represent a biological system to which the therapy can be applied. Examples of biological systems that can be represented by the computer model include a cell, a tissue, an organ, a multi-cellular organism, and a population of cellular or multi-cellular organisms.
  • the first step shown in FIG. 2 is to define a virtual therapy associated with the therapy (step 200).
  • the virtual therapy can be defined to simulate the therapy.
  • the virtual therapy can define a modification to the computer model to simulate the therapy.
  • the second step shown in FIG. 2 is to use the virtual therapy to identify one or more biomarkers of the therapy (step 202).
  • a set i.e., one or more
  • virtual measurements can be defined. Each virtual measurement of the set of virtual measurements can be associated with a different measurement for the biological system.
  • the set of virtual measurements can include virtual measurements that are configured to evaluate the behavior of the computer model absent the virtual therapy as well as based on the virtual therapy.
  • the computer model can be executed to produce a set of results of the set of virtual measurements. Once produced, the set of results can be analyzed to identify one or more biomarkers of the therapy.
  • FIG. 3 illustrates the architecture of a computer model 300 that can be used to identify biomarkers of a therapy in accordance with an embodiment of the invention.
  • the computer model 300 can represent a biological system to which the therapy can be applied.
  • the computer model 300 can be defined as, for example, described in the patent to Paterson et al., entitled “Method of Managing Objects and Parameter Values Associated with the Objects Within a Simulation Model", U.S. Patent No. 6,078,739, issued on June 20, 2000; the patent to Fink et al., entitled “Hierarchical Biological Modelling System and Method", U.S. Patent No. 5,657,255, issued on August 12, 1997; the co-owned and co-pending patent application to Kelly et al., entitled “Method and Apparatus for Computer Modeling of an Adaptive Immune Response", U.S. Application Serial No.
  • the computer model 300 can be defined as in commercially available computer models such as, for example, Entelos® Asthma PhysioLab® systems, Entelos® Obesity PhysioLab® systems, and Entelos® Adipocyte CytoLabTM systems.
  • the computer model 300 can include a mathematical model that represents a set of dynamic processes using a set of mathematical relations.
  • the computer model 300 can represent a set of biological processes associated with the biological system using a set of mathematical relations.
  • the computer model 300 can represent a first biological process using a first mathematical relation and a second biological process using a second mathematical relation.
  • the computer model 300 can represent biological processes associated with an immune response to various antigens.
  • a mathematical relation typically includes one or more variables the behavior (e.g., time evolution) of which can be simulated by the computer model 300.
  • mathematical relations of the computer model 300 can define interactions among variables, where the variables can represent biological attributes associated with inter-cellular constituents, cellular constituents, intra-cellular constituents, or a combination thereof, that make up the biological system.
  • Constituents can include, for example, metabolites; DNA;
  • RNA proteins; enzymes; hormones; cells; organs; tissues; portions of cells, tissues, or organs; subcellular organelles; chemically reactive molecules like H + ; superoxides; ATP; citric acid; protein albumin; as well as combinations or aggregate representations of these constituents.
  • variables can represent various stimuli that can be applied to the biological system.
  • the behavior of variables can be influenced by a set of parameters included in the computer model 300.
  • parameters can include initial values of variables, half-lives of variables, rate constants, conversion ratios, exponents, and curve-fitting parameters.
  • the set of parameters can be included in the mathematical relations of the computer model 300.
  • parameters can be used to represent intrinsic properties (e.g., genetic factors or susceptibilities) as well as external influences (e.g., environmental factors) for the biological system.
  • the mathematical relations employed in the computer model 300 can include, for example, ordinary differential equations, partial differential equations, stochastic differential equations, differential algebraic equations, difference equations, cellular automata, coupled maps, equations of networks of Boolean, fuzzy logical networks, or a combination thereof.
  • the computer model 300 can be configured to simulate the behavior of variables by, for example, numerical or analytical integration of one or more mathematical relations. For example, numerical integration of the ordinary differential equations defined above can be performed to obtain values for the variables at various times.
  • the computer model 300 can be configured to allow visual representation of the mathematical relations as well as interrelationships between variables, parameters, and processes.
  • This visual representation can include multiple modules or functional areas that, when grouped together, represent a large complex model of the biological system.
  • the computer model 300 can be used to define one or more configurations.
  • the computer model 300 can be used to define one or more configurations.
  • configuration 300 is shown defining configuration A 302, configuration B 304, and configuration C 306. While three configurations are shown in FIG. 3, it should be recognized that more or less configurations can be defined depending on the specific application.
  • Various configurations of the computer model 300 can be associated with different representations of the biological system.
  • various configurations of the computer model 300 can represent, for example, different variations of the biological system having different intrinsic properties, different external influences, or both.
  • the observable condition (e.g., an outward manifestation) of the biological system can be referred to as its phenotype, while the underlying conditions of the biological system that give rise to the phenotype can be based on genetic factors, environmental factors, or both.
  • phenotypes of a biological system can be defined with varying degrees of specificity.
  • An example of such an observable condition or phenotype might be an asthmatic condition or, more specifically, a moderate asthmatic condition that can be exhibited by an individual.
  • a particular phenotype typically can be reproduced by different underlying conditions (e.g., different combinations of genetic and enviromnental factors). For example, while two individuals may appear to be similarly asthmatic, one could be asthmatic because of genetic factors, and the other could be asthmatic because of environmental factors.
  • various configurations of the computer model 300 can be defined to represent different underlying conditions giving rise to a particular phenotype of the biological system. Alternatively, or in conjunction, various configurations of the computer model 300 can be defined to represent different phenotypes of the biological system.
  • configurations of the computer model 300 may be referred to as virtual patients.
  • configurations A 302, B 304, and C 306 may be referred to as virtual patients A, B, and C, respectively.
  • a virtual patient can be defined to represent a human patient having a phenotype based on a particular combination of underlying conditions.
  • Various virtual patients can be defined to represent human patients having the same phenotype but based on different underlying conditions.
  • various virtual patients can be defined to represent human patients having different phenotypes.
  • a configuration of the computer model 300 can be associated with a particular set of values for the parameters of the computer model 300.
  • configuration A 302 may be associated with a first set of parameter values
  • configuration B 304 may be associated with a second set of parameter values that differs in some fashion from the first set of parameter values.
  • the second set of parameter values may include at least one parameter value differing from a corresponding parameter value included in the first set of parameter values.
  • configuration C 306 may be associated with a third set of parameter values that differs in some fashion from the first and second set of parameter values.
  • One or more configurations of the computer model 300 can be created based on an initial configuration that is associated with initial parameter values.
  • a different configuration can be created based on the initial configuration by introducing a modification to. the initial configuration.
  • Such modification can include, for example, a parametric change (e.g., altering or specifying one or more initial parameter values), altering or specifying behavior of one or more variables, altering or specifying one or more functions representing interactions among variables, or a combination thereof.
  • a parametric change e.g., altering or specifying one or more initial parameter values
  • altering or specifying behavior of one or more variables altering or specifying one or more functions representing interactions among variables, or a combination thereof.
  • Alternative parameter values can be defined as, for example, disclosed in, U.S. Patent No. 6,078,739 discussed previously.
  • parameter values can be grouped into different sets of parameter values that can be used to define different configurations of the computer model 300.
  • the initial configuration itself can be created based on another configuration (e.g., a different initial configuration) in a manner as discussed above.
  • one or more configurations of the computer model 300 can be created based on an initial configuration using linked simulation operations as, for example, disclosed in the co-pending and co-owned patent application to Paterson et al., entitled "Method and Apparatus for Conducting Linked Simulation
  • various configurations of the computer model 300 can represent variations of the biological system that are sufficiently different to evaluate the effect of such variations on how the biological system responds to the therapy.
  • one or more biological processes represented by the computer model 300 can be identified as playing a role in modulating biological response to the therapy, and various configurations can be defined to represent different modifications of the one or more biological processes.
  • the identification of the one or more biological processes can be based on, for example, experimental or clinical data, scientific literature, results of a computer model, or a combination thereof.
  • various configurations can be created by defining different modifications to one or more mathematical relations included in the computer model 300, which one or more mathematical relations represent the one or more biological processes.
  • a modification to a mathematical relation can include, for example, a parametric change (e.g., altering or specifying one or more parameter values associated with the mathematical relation), altering or specifying behavior of one or more variables associated with the mathematical relation, altering or specifying one or more functions associated with the mathematical relation, or a combination thereof.
  • the computer model 300 may be executed based on a particular modification for a time sufficient to create a "stable" configuration of the computer model 300.
  • a biological process that modulates biological response to the therapy can be associated with a knowledge gap or uncertainty, and various configurations of the computer model 300 can be defined to represent different plausible hypotheses or resolutions of the knowledge gap.
  • biological processes associated with airway smooth muscle (ASM) contraction can be identified as playing a role in modulating biological response to a therapy for asthma. While it may be understood that inflammatory mediators have an effect on ASM contraction, the relative effects of the different types of inflammatory mediators on ASM contraction as well as baseline concentrations of the different types of inflammatory mediators may not be well understood.
  • various configurations can be defined to represent human patients having different baseline concentrations of inflammatory mediators.
  • FIG. 4 illustrates an example of a user-interface screen indicating a virtual patient 400 that can be defined to represent a human patient, hi this example, the virtual patient 400 labeled as "Patient A" is defined to represent a moderate asthmatic patient.
  • various parameter values e.g., parameter values associated with epithelium production magnitude, eosinophil priming response, constant background antigen (Ag), and so forth
  • parameters values associated with production levels for various types of inflammatory mediators can be specified to represent a moderate asthmatic patient having particular baseline concentrations of the various types of inflammatory mediators.
  • basophil inflammatory mediators As shown in the "Experiment Protocol" window 402, increased or decreased production levels can be specified for basophil inflammatory mediators, sensory nerve inflammatory mediators, eosinophil CysLT mediators, epithelial inflammatory mediators, bradykinin mediators, macrophage inflammatory mediators, and mast cell inflammatory mediators.
  • FIG. 5 illustrates an example of a user-interface screen indicating another virtual patient 500 that can be defined to represent a different human patient.
  • the virtual patient 500 labeled as "Patient B” is defined to represent a different moderate asthmatic patient.
  • various parameter values e.g., parameter values associated with epithelium production magnitude, eosinophil priming response, constant background antigen (Ag), and so forth
  • Ag constant background antigen
  • a different set of parameter values associated with production levels for various types of inflammatory mediators is specified to represent a moderate asthmatic patient having different baseline concentrations of the various types of inflammatory mediators.
  • one or more configurations of the computer model 300 can be validated with respect to the biological system represented by the computer model 300.
  • Validation typically refers to a process of establishing a certain level of confidence that the computer model 300 will behave as expected when compared to actual, predicted, or desired data for the biological system.
  • various configurations of the computer model 300 can be validated with respect to one or more phenotypes of the biological system. For instance, configuration A 302 can be validated with respect to a first phenotype of the biological system, and configuration B 304 can be validated with respect to the first phenotype or a second phenotype of the biological system that differs in some fashion from the first phenotype.
  • One or more configurations of the computer model 300 can be validated using a set of virtual stimuli as, for example, disclosed in the co-pending and co-owned patent application to Paterson, entitled "Apparatus and Method for Validating a Computer
  • a virtual stimulus can be associated with a stimulus or perturbation that can be applied to a biological system.
  • Different virtual stimuli can be associated with stimuli that differ in some fashion from one another.
  • Stimuli that can be applied to a biological system can include, for example, existing or hypothesized therapeutic agents, treatment regimens, and medical tests. Additional examples of stimuli include exposure to existing or hypothesized disease precursors. Further examples of stimuli include environmental changes such as those relating to changes in level of exposure to an environmental agent (e.g., an antigen), changes in feeding behavior, and changes in level of physical activity or exercise.
  • an environmental agent e.g., an antigen
  • a virtual stimulus may be referred to as a stimulus- response test.
  • a set of stimulus-response tests By applying a set of stimulus-response tests to a configuration of the computer model 300, a set of results of the set of stimulus-response tests can be produced.
  • the configuration can be validated if the set of results of the set of stimulus-response tests sufficiently conforms to a set of expected results of the set of stimulus-response tests.
  • An expected result of a stimulus-response test can be based on actual, predicted, or desired behavior of a biological system when subjected to a stimulus associated with the stimulus- response test.
  • an expected result of a stimulus-response test typically will be based on actual, predicted, or desired behavior for the phenotype of the biological system.
  • the behavior of a biological system can be, for example, an aggregate behavior of the biological system or behavior of a portion of the biological system when subjected to a particular stimulus.
  • an expected result of a stimulus- response test can be based on experimental or clinical behavior of a biological system when subjected to a stimulus associated with the stimulus-response test.
  • an expected result of a stimulus-response test can include an expected range of behavior associated with a biological system when subjected to a particular stimulus. Such range of behavior can arise, for example, as a result of variations of the biological system having different intrinsic properties, different external influences, or both.
  • a stimulus-response test can be created by defining a modification to one or more mathematical relations included in the computer model 300, which one or more mathematical relations can represent one or more biological processes affected by a stimulus associated with the stimulus-response test.
  • a stimulus-response test can define a modification that is to be introduced statically, dynamically, or a combination thereof, depending on the type of stimulus associated with the stimulus-response test.
  • a modification can be introduced statically by replacing one or more parameter values with one or more modified parameter values associated with a stimulus.
  • a modification can be introduced dynamically to simulate a stimulus that is applied in a time-varying manner (e.g., a stepwise manner or a periodic manner).
  • a modification can be introduced dynamically by altering or specifying parameter values at certain times or for a certain time duration.
  • a stimulus-response test can be applied to one or more configurations of the computer model 300 using linked simulation operations as described in U.S. Application Serial No. 09/814,536 discussed previously. For instance, an initial simulation operation may be performed for a configuration, and, following introduction of a modification defined by a stimulus-response test, one or more additional simulation operations that are linked to the initial simulation operation may be performed for the configuration.
  • FIG. 6 illustrates an example of a user-interface screen indicating various stimulus-response tests that can be defined.
  • various stimulus-response tests e.g., stimulus-response tests 620, 622, and 624.
  • a folder 610 labeled as "Stimulus-response tests for Patient A"
  • the various stimulus-response tests can define modifications to a virtual patient 600 labeled as "Patient
  • various stimulus-response tests can be grouped under folders 612, 614, 616, and 618 that are associated with virtual patients 602, 604, 606, and 608, respectively.
  • the folders 610, 612, 614, 616, and 618 can include one or more stimulus- response tests in common.
  • FIG. 7 illustrates an example of a user-interface screen indicating how a stimulus-response test 700 can be defined.
  • a set of virtual measurements can be defined such that a set of results of a set of stimulus-response tests can be produced for a particular configuration of the computer model 300.
  • Multiple virtual measurements can be defined, and a result can be produced for each of the virtual measurements.
  • a virtual measurement can be associated with a measurement for a biological system. Examples of measurements can include existing or hypothesized measurements (e.g., experimental or clinical measurements) to evaluate various biological attributes of the biological system. Different virtual measurements can be associated with measurements that differ in some fashion from one another. For instance, different measurements can be configured to evaluate different biological attributes of a biological system. Alternatively, or in conjunction, different measurements can be configured to evaluate the same biological attribute of a biological system under different conditions (e.g., at different times). [0052] In the present embodiment of the invention, a virtual measurement can be
  • variables of the computer model 300 can represent various biological attributes of the biological system.
  • a virtual measurement can simulate a measurement of a biological attribute and can be defined based on one or more variables that represent the biological attribute in the computer model 300.
  • a virtual measurement can be defined based on the value of one or more variables or based on the value of a function of one or more variables.
  • virtual measurements can include a value at one or more times; an absolute or relative increase in a value over a time interval; an absolute or relative decrease in a value over a time interval; average value; minimum value; maximum value; time at minimum value; time at maximum value; area below a curve when values are plotted along a given axis (e.g., time); area above a curve when values are plotted along a given axis (e.g., time); pattern or trend associated with a curve when values are plotted along a given axis (e.g., time); rate of change of a value; average rate of change of a value; curvature associated with a value; number of instances a value exceeds, reaches, or falls below another value (e.g., a predefined value) over a time interval; minimum difference between a value and another value (e.g., a predefined value) over a time interval; maximum difference between a value and another value (e.g., a predefined value) over a
  • FIG. 8 illustrates an example of a user-interface screen indicating various virtual measurements that can be defined.
  • a stimulus-response test
  • Curve and "Late Phase Area above Curve” can be defined and are grouped under the folder 804.
  • the virtual measurements 810, 812, 814, and 816 characterize the behavior of a Forced Expiratory Volume in 1 second (FEV1) curve for a particular configuration of a computer model to which the stimulus-response test 800 is applied.
  • the virtual measurements 810, 812, 814, and 816 can be defined based on a variable that represents FEV1 in the computer model.
  • results 818, 820, 822, and 824 of the virtual measurements 810, 812, 814, and 816 can be produced based on applying the stimulus-response test 800 to the configuration.
  • the results 818, 820, 822, and 824 of the virtual measurements 810, 812, 814, and 816 can be compared with expected results of the virtual measurements 810, 812, 814, and 816 to validate the configuration.
  • the stimulus-response test 800 can be applied to one or more additional configurations to validate the one or more additional configurations.
  • a configuration can be deemed to be validated with respect to a biological system if a certain number (e.g., a majority or all) of results of a set of results for the configuration are substantially consistent with expected results associated with the biological system. It should be recognized that a result of a stimulus-response test can be substantially consistent with an expected result without being identical to the expected result. For instance, a result of a stimulus-response test can be substantially consistent with an expected result if the difference between the two results falls within a certain range (e.g., within 20 percent or within 10 percent of the expected result).
  • a result of a stimulus-response test can be substantially consistent with an expected result if the two results exhibit similar relative changes that can have different absolute values.
  • an expected result can include an expected range of behavior, and a result of a stimulus-response test can be substantially consistent with the expected result if the result of the stimulus-response test falls within the expected range of behavior.
  • FIG. 9 illustrates an example of a user-interface screen indicating plots of FEVl curves 900 and 902 with respect to time.
  • FEVl curve 900 represents the behavior of a reference moderate asthmatic patient that is exposed to an antigen challenge.
  • FEVl curve 902 represents a result of a stimulus-response test simulating the antigen challenge for a particular configuration of a computer model.
  • FEVl curve 902 can be deemed to be substantially consistent with FEVl curve 900, and the configuration can be validated with respect to the reference moderate asthmatic patient.
  • the behavior of the various configurations can be used for predictive analysis.
  • one or more configurations can be used to predict behavior of a biological system when subjected to various stimuli.
  • a virtual therapy associated with a therapy can be applied to a configuration in an attempt to predict how a real-world equivalent of the configuration would respond to the therapy.
  • Therapies that can be applied to a biological system can include, for example, existing or hypothesized therapeutic agents and treatment regimens.
  • a virtual therapy can be created in a manner similar to that used to create a stimulus-response test.
  • a virtual therapy can be created, for example, by defining a modification to one or more mathematical relations included in a computer model, which one or more mathematical relations can represent one or more biological processes affected by a therapy associated with the virtual therapy.
  • a virtual therapy can define a modification that is to be introduced statically, dynamically, or a combination thereof, depending on the particular therapy associated with the virtual therapy.
  • a virtual therapy can be applied to one or more configurations of a computer model using linked simulation operations as described in U.S. Application Serial No. 09/814,536 discussed previously.
  • the virtual therapy 1000 can define a modification with respect to various parameter values (e.g., parameter values associated with basophil functions, macrophage functions, and T-cell functions) to simulate the therapy.
  • the virtual therapy 1000 can also define a modification to simulate application of muscarinic agonist, on-line PC20 (i.e., a methacholine challenge), and an antigen challenge to evaluate post-therapy behavior of the virtual patient 1002.
  • a set of virtual measurements can be defined such that a set of results of a virtual therapy can be produced for a particular configuration. Multiple virtual measurements can be defined, and a result can be produced for each of the virtual measurements.
  • a virtual measurement can be associated with a measurement for a biological system, and different virtual measurements can be associated with measurements that differ in some fashion from one another.
  • a set of virtual measurements can include a first set of virtual measurements and a second set of virtual measurements.
  • the first set of virtual measurements can be defined to evaluate the behavior of one or more configurations absent the virtual therapy, while the second set of virtual measurements can be defined to evaluate the behavior of the one or more configurations based on the virtual therapy.
  • the first and second set of virtual measurements can be associated with measurements configured to evaluate different biological attributes of a biological system.
  • the first and second set of virtual measurements can be associated with measurements configured to evaluate the same biological attributes of the biological system under different conditions.
  • the first set of virtual measurements can include a first virtual measurement that is associated with a first measurement
  • the second set of virtual measurements can include a second virtual measurement that is associated with a second measurement.
  • the first measurement can be configured to evaluate a first biological attribute of the biological system absent the therapy
  • the second measurement can be configured to evaluate the first biological attribute or a second biological attribute based on the therapy.
  • FIG. 11 illustrates an example of a user-interface screen indicating various virtual measurements (e.g., virtual measurements 1100, 1102, 1104, 1106, 1108, and 1110) that can be defined to evaluate the behavior of a configuration of a computer model absent a virtual therapy and based on the virtual therapy.
  • virtual measurements 1100, 1102, 1104, 1106, 1108, and 1110 can be defined to evaluate the behavior of a configuration of a computer model absent a virtual therapy and based on the virtual therapy.
  • the various virtual measurements can be defined based on variables in the computer model that represent biological attributes associated with endothelial cell surface E-selectin, endothelial cell surface ICAM-1, endothelial cell surface P-selectin, and endothelial cell surface VCAM-1.
  • the various virtual measurements can be defined for various times.
  • the virtual measurements 1100, 1104, and 1108 are defined for an initial time (e.g., Day 0) and are configured to evaluate the behavior of the configuration absent the virtual therapy (e.g., prior to applying the virtual therapy at Day 0).
  • Other virtual measurements e.g., the virtual measurements 1102, 1106, and 1110 are defined for subsequent times (e.g., after Day 0) and are configured to evaluate the behavior of the configuration based on the virtual therapy. As shown in FIG. 11, various results (e.g., results 1112, 1114, 1116, 1118, 1120, and 1122) of the various virtual measurements can be produced for the configuration.
  • FIG. 12 illustrates another example of a user-interface screen indicating various virtual measurements (e.g., virtual measurements 1200, 1202, and 1204) that can be defined to evaluate the behavior of a configuration of a computer model absent a virtual therapy and based on the virtual therapy.
  • the various virtual measurements can be defined based on a variable in the computer model that represents
  • FIG. 13 illustrates a flow chart to identify one or more biomarkers using a virtual therapy, according to an embodiment of the invention.
  • the first step shown in FIG. 13 is to execute a computer model absent the virtual therapy to produce a first set of results (step 1300).
  • a first set of virtual measurements can be defined to evaluate the behavior of one or more configurations of the computer model absent the virtual therapy.
  • the first step (step 1300) can entail applying the first set of virtual measurements to one or more configurations to produce the first set of results.
  • Each virtual measurement of the first set of virtual measurements can be associated with a different measurement for a biological system absent the therapy.

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Abstract

L'invention concerne un appareil et un procédé pour identifier des biomarqueurs au moyen d'un modèle informatisé. Dans un mode de réalisation, un code logiciel exécutable sur ordinateur comprend un code destiné à définir un ensemble de configurations associées à un modèle informatisé d'un système biologique. Le code logiciel exécutable sur ordinateur comprend également un code destiné à appliquer une mesure virtuelle à chaque configuration d'un ensemble de configurations et un code pour appliquer une thérapie virtuelle à l'ensemble de configurations pour produire un résultat de la thérapie virtuelle pour chaque configuration faisant partie de l'ensemble de configurations. Le code logiciel exécutable sur ordinateur comprend aussi un code pour identifier la corrélation entre les résultats de la mesure virtuelle pour l'ensemble de configurations et les résultats de la thérapie virtuelle pour l'ensemble de configurations.
PCT/US2003/039522 2002-12-12 2003-12-11 Appareil et procede pour identifier des marqueurs biologiques au moyen d'un modele informatise Ceased WO2004055636A2 (fr)

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US20050033521A1 (en) 2005-02-10

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